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You surf the internet, and you see the error “503 (Service Unavailable.)” This tends to happen when you are about to do something important like submitting an order on a website. You then wait for long, and an error message shows up afterward.
As a developer, of course, you want to have a service that is used heavily. But when it comes to system loads, various factors come into place. It can start from the time of day, the end of month processing, press releases, batch runs at night, and many more.
So, how can you safeguard your API to work even when the load is heavy? How do you keep your customers hooked to your service? As a customer using an application to perform a financial transaction, you want the app to work seamlessly. If as a developer you cannot guarantee your users the reliability of your service at heavy loads, you will lose your credibility and authority.
To avoid these scenarios, you need to know how to test restful APIs. There are two ways of many that you can use to load test to an API. Each of these approaches has drawbacks and advantages that can help you have a more reliable service.
API – Mocking
This method is by far the least helpful. Developers use it for testing the unit early enough. Mocks return responses that are hard-coded, the formats change during the development phase. The mocks are disposable because once the real API is complete, the code is discarded. In other words, the time and effort used to create the mock are wasted.
Since mocks do not tap into the real data sources, their performances do not represent real-world applications. In summary, mocks are not really good at representing real-world use, but they are suitable for unit testing.
A full test environment
For the mock approach, the API is assumed to be incomplete, or that no data source exists. Once the API is ready for testing, the clone approach is a good option. In this approach, the API will undergo a full test using a portion of the production data.
In most cases, you will have a copy of all the apps running because your source is dependent on other API’s, not just the queries for the database. This approach helps to represent a live performance of the system. If the test humbles the system, it hurts on one.
For starters, the production data often have sensitive information about the data, so regulations of data privacy can be effected. Additionally, if the API uses pay-per-use services, the testing can be expensive. Finally, the production data can still be used in a clone environment; when you want to perform the test once again, you will reload the test data yet.
Cloned environments load test results that are near-production without harming production if the test crashes the system. However, privacy concerns are raised, constant data reloading is done, and it can become expensive if you include third-party apps.
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